Load data

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## randomForest 4.7-1.1
## Type rfNews() to see new features/changes/bug fixes.
## 
## Attaching package: 'randomForest'
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Set parameter ranges

# Vectors of all possible conditions combinations
frs <- as.numeric(as.character(unique(CC.TotalData$Flow.rate)))
chls <- as.numeric(as.character(unique(CC.TotalData$Chlorophyll)))
guans <- c(1,2)
lights <- c(1,2)
conditions <- expand.grid(frs,chls,guans,lights)

Loop through combinations

for (i in 1:dim(conditions)[1])
{
  velocity <- CC.TotalData$v[
  (CC.TotalData$Flow.rate==conditions[i,1] & 
     CC.TotalData$Chlorophyll==conditions[i,2] & 
     as.numeric(CC.TotalData$Guano)==conditions[i,3] &
     as.numeric(CC.TotalData$Light)==conditions[i,4])]
  if (length(velocity) <= 1) {
    conditions[i,5] <- NA } else {
      c<-cor.test(log10(velocity[1:length(velocity)-1]),
                  log10(velocity[2:length(velocity)]))
      conditions[i,5] <- c$estimate
        v0 <- log10(velocity[1:length(velocity)-1])
        v1 <- log10(velocity[2:length(velocity)])
        df <- data.frame(v0,v1)
        fit <- lm(v1 ~ v0 + 1, data = df)
        conditions[i,6] <- fit$coefficients[1]
        conditions[i,7] <- fit$coefficients[2]
        conditions[i,8] <- sd(fit$residuals)
        d <- dip.test(velocity)
        conditions[i,9] <- d$p.value
        hist(log10(velocity),100,
             main=d$p.value)
  }  
}
## n = 258078 > max_n{n in table} = 72000 -- using that as asymptotic value.

## n = 116154 > max_n{n in table} = 72000 -- using that as asymptotic value.

## n = 266817 > max_n{n in table} = 72000 -- using that as asymptotic value.

## n = 211577 > max_n{n in table} = 72000 -- using that as asymptotic value.

## n = 253471 > max_n{n in table} = 72000 -- using that as asymptotic value.

## n = 192727 > max_n{n in table} = 72000 -- using that as asymptotic value.

## n = 271850 > max_n{n in table} = 72000 -- using that as asymptotic value.

## n = 91500 > max_n{n in table} = 72000 -- using that as asymptotic value.

## n = 137251 > max_n{n in table} = 72000 -- using that as asymptotic value.

## n = 282203 > max_n{n in table} = 72000 -- using that as asymptotic value.

## n = 140189 > max_n{n in table} = 72000 -- using that as asymptotic value.

## n = 142013 > max_n{n in table} = 72000 -- using that as asymptotic value.

conditions <- setNames(conditions,c("flow.rate","chlorophyll","guano","light","corr.val","slope","intercept","sigma","dip.test"))

Using random forest to fit the missing values for autocorrelation

Idata <- which(!is.na(conditions[,5])) # Index of data
Iblank <- which(is.na(conditions[,5])) # Index of blank data

conditions_data <- conditions[Idata,]
conditions_blank <- conditions[Iblank,]

conditions.rf.slope <- randomForest(slope ~ .,
                              data=select(conditions_data,c(1,2,3,4,6)),
                              importance=TRUE,
                              proximity=TRUE)
plot(conditions.rf.slope)

varImpPlot(conditions.rf.slope)

conditions_blank$slope <- predict(conditions.rf.slope,conditions_blank[,1:4])
fit.slope <- predict(conditions.rf.slope,conditions_data[,1:4])
plot(fit.slope,conditions_data$slope)

plot(conditions_blank$chlorophyll,conditions_blank$slope)

plot(conditions_blank$flow.rate,conditions_blank$slope)

plot(conditions_blank$light,conditions_blank$slope)

plot(conditions_blank$guano,conditions_blank$slope)

conditions.rf.intercept <- randomForest(intercept ~ .,
                              data=select(conditions_data,c(1,2,3,4,7)),
                              importance=TRUE,
                              proximity=TRUE)
plot(conditions.rf.intercept)

varImpPlot(conditions.rf.intercept)

conditions_blank$intercept <- predict(conditions.rf.intercept,conditions_blank[,1:4])
fit.intercept <- predict(conditions.rf.intercept,conditions_data[,1:4])
plot(fit.intercept,conditions_data$intercept)

plot(conditions_blank$chlorophyll,conditions_blank$intercept)

plot(conditions_blank$flow.rate,conditions_blank$intercept)

plot(conditions_blank$light,conditions_blank$intercept)

plot(conditions_blank$guano,conditions_blank$intercept)

conditions.rf.sigma <- randomForest(sigma ~ .,
                              data=select(conditions_data,c(1,2,3,4,8)),
                              importance=TRUE,
                              proximity=TRUE)
plot(conditions.rf.sigma)

varImpPlot(conditions.rf.sigma)

conditions_blank$sigma <- predict(conditions.rf.sigma,conditions_blank[,1:4])
fit.sigma <- predict(conditions.rf.sigma,conditions_data[,1:4])
plot(fit.sigma,conditions_data$sigma)

plot(conditions_blank$chlorophyll,conditions_blank$sigma)

plot(conditions_blank$flow.rate,conditions_blank$sigma)

plot(conditions_blank$light,conditions_blank$sigma)

plot(conditions_blank$guano,conditions_blank$sigma)

Save fit models

save(conditions.rf.slope,conditions.rf.intercept,conditions.rf.sigma, file='notebook13-rf.RData')